The interplay between governmental communications and fellow citizens’ reactions via twitter: Experimental results of a theoretical crisis in the Netherlands

This study aimed to gain insight into the interplay between citizens’ reactions on Twitter and governmental communications as well as their effects on self‐reliant behaviour and trust. Two experimental studies were conducted. In Study 1, participants first received other citizens’ reactions followed by the government's communications about how to act. Participants received supporting, opposing, mixed, or no reactions from other citizens. In Study 2, participants first received the government's communications with either certain or uncertain crisis information, followed by the different citizens’ reactions. The results showed that citizens’ reactions via Twitter are not necessarily detrimental to the effectiveness of governmental communications regarding self‐reliant behaviour. In addition, our findings suggest being careful with providing uncertain governmental communications during a crisis.     

Mobile Social Net work--Research and Implementation of Mobile Twitter System

This paper researched and analyzedweb2.0 technology and mobile social network.Then researched and implemented the mobile twitter system.This paper introduces the function and modules of mobile client and PC server respectively.We also had the user experience and system test which are wrote in this paper.     

Understanding the Language of ISIS: An Empirical Approach to Detect Radical Content on Twitter Using Machine Learning

The internet, particularly online social networking platforms have revolutionized the way extremist groups are influencing and radicalizing individuals. Recent research reveals that the process initiates by exposing vast audiences to extremist content and then migrating potential victims to confined platforms for intensive radicalization. Consequently, social networks have evolved as a persuasive tool for extremism aiding as recruitment platform and psychological warfare. Thus, recognizing potential radical text or material is vital to restrict the circulation of the extremist chronicle. The aim of this research work is to identify radical text in social media. Our contributions are as follows: (i) A new dataset to be employed in radicalization detection; (ii) In depth analysis of new and previous datasets so that the variation in extremist group narrative could be identified; (iii) An approach to train classifier employing religious features along with radical features to detect radicalization; (iv) Observing the use of violent and bad words in radical, neutral and random groups by employing violent, terrorism and bad words dictionaries. Our research results clearly indicate that incorporating religious text in model training improves the accuracy, precision, recall, and F1-score of the classifiers. Secondly a variation in extremist narrative has been observed implying that usage of new dataset can have substantial effect on classifier performance. In addition to this, violence and bad words are creating a differentiating factor between radical and random users but for neutral (anti-ISIS) group it needs further investigation.     

ADC对接收机测量性能的影响

作为模拟信号通向数字信号的桥梁,ADC是数字接收机发展的关键和瓶颈所在。为了研究ADC的采样时钟抖动及量化位数对接收机测量性能的影响程度。采用了理论分析并数学建模的方法,通过Matlab仿真实验,得到了ADC的采样时钟抖动及量化对接收机性能的影响程度,得出结论：相较于量化（N≥4）,采样时钟抖动对接收机的测量性能影响更大。相比于之前独立分析ADC采样时钟抖动及量化对接收机的影响,使用Matlab Guide将两者联合仿真。     

Making Big Data Small: Strategies to Expand Urban and Geographical Research Using Social Media

While exciting, Big Data (particularly geotagged social media data) has proven difficult for many urbanists and social science researchers to use. As a partial solution, we propose a strategy that enables the fast extracting of only relevant data from large sets of geosocial data. While contrary to many Big Data approaches—in which analysis is done on the entire dataset—much productive social science work can use smaller datasets—around the same size as census or survey data—within standard methodological frameworks. The approach we outline in this paper—including the example of a fully operating system—offers a solution for urban researchers interested in these types of data but reluctant to personally build data science skills.     

Information Extraction From Microblogs: A Survey

Microblogging(e.g. Twitter, http://twitter.com), as a new form of online communication in which users talk about their daily lives, publish opinions or share information by short posts, has become one of the most popular social networking services today, which makes it potentially a large information base attracting increasing attention of researchers in the field of knowledge discovery and data mining. In this paper, we conduct a survey about existing research on information extraction from microblogging services and their applications, and then address some promising future works. We specifically analyze three types of information: personal, social and travel information.     

Who is Who on Twitter–Spammer,Fake or Compromised Account? A Tool to Reveal True Identity in Real-Time

Social networks once being an innoxious platform for sharing pictures and thoughts among a small online community of friends has now transformed into a powerful tool of information, activism, mobilization, and sometimes abuse. Detecting true identity of social network users is an essential step for building social media an efficient channel of communication. This paper targets the microblogging service, Twitter, as the social network of choice for investigation. It has been observed that dissipation of pornographic content and promotion of followers market are actively operational on Twitter. This clearly indicates loopholes in the Twitter’s spam detection techniques. Through this work, five types of spammers-sole spammers, pornographic users, followers market merchants, fake, and compromised profiles have been identified. For the detection purpose, data of around 1 Lakh Twitter users with their 20 million tweets has been collected. Users have been classified based on trust, user and content based features using machine learning techniques such as Bayes Net, Logistic Regression, J48, Random Forest, and AdaBoostM1. The experimental results show that Random Forest classifier is able to predict spammers with an accuracy of 92.1%. Based on these initial classification results, a novel system for real-time streaming of users for spam detection has been developed. We envision that such a system should provide an indication to Twitter users about the identity of users in real-time.     

Multilingual Sentiment Mining System to Prognosticate Governance

In the age of the internet, social media are connecting us all at the tip of our fingers. People are linkedthrough different social media. The social network, Twitter, allows people to tweet their thoughts on any particular event or a specific political body which provides us with a diverse range of political insights. This paper serves the purpose of text processing of a multilingual dataset including Urdu, English, and Roman Urdu. Explore machine learning solutions for sentiment analysis and train models, collect the data on government from Twitter, apply sentiment analysis, and provide a python library that classifies text sentiment. Training data contained tweets in three languages: English: 200k, Urdu: 200k and Roman Urdu: 11k. Five different classification models are applied to determine sentiments, and eventually, the use of ensemble technique to move forward with the acquired results is explored. The Logistic Regression model performed best with an accuracy of 75%, followed by the Linear Support Vector classifier and Stochastic Gradient Descent model, both having 74% accuracy. Lastly, Multinomial Naïve Bayes and Complement Naïve Bayes models both achieved 73% accuracy.     

微博文本处理研究综述

微博是一个基于关系的信息分享、传播以及获取平台。用户可以通过WEB、WAP以及各种客户端组件,以140字左右的文字更新信息,并实现即时分享。由于微博发展迅猛,微博文本已经形成了大规模积累,针对微博文本的研究已经成为了一个十分重要的课题。该文对微博文本进行了定义,阐述了微博文本研究的重要性,并从微博文本的不同应用领域出发,对微博文本的研究现状进行了综述,介绍了目前已经存在的微博文本数据集和应用系统。     

基于句子语义距离的释义识别研究

针对释义识别任务如何学习上下文语义的问题,提出了利用词向量来表示句子语义距离的模型。首先,利用word2vec训练大规模的词向量模型,把词的语义信息利用向量分布式表示;然后通过欧氏距离来计算句子间词的移动开销;最后基于EMD模型实现了从词语义距离到句子语义距离的建模,通过采用句子变换矩阵来实现句子间语义距离的度量,进而从语义相似性方面进行句子释义识别。实验基于SemEval-2015 PIT任务,与作为实验基线的逻辑回归和加权矩阵因数分解方法进行比较,提出的模型采用有监督实验时, 值非常接近实验基线,而采用无监督方法实验时, 值提高了5.8%。